Data Smoothing Numerical Methods and Their Applications in Unsupervised Learning for Prediction of Diabetes in Patients | Zendy

Satish Kumar Soni | Zendy; Ramjeevan Singh Thakur | Zendy; Anil Gupta | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Data Smoothing Numerical Methods and Their Applications in Unsupervised Learning for Prediction of Diabetes in Patients

Author(s) -

Satish Kumar Soni,

Ramjeevan Singh Thakur,

Anil Gupta

Publication year - 2019

Publication title -

international journal of innovative technology and exploring engineering

Language(s) - English

Resource type - Journals

ISSN - 2278-3075

DOI - 10.35940/ijitee.i8196.078919

Subject(s) - smoothing , unsupervised learning , computer science , machine learning , diabetes mellitus , artificial intelligence , medicine , computer vision , endocrinology

Machine Learning in its fullest can provide much more accurate and enhanced analysis for medical diagnosis. In this paper we are trying to portray how the data related to diabetes can be used to predict if a person has diabetes or not. In more specific way this paper will explore the utilization of numerical methods smoothing with unsupervised learning to predict the early signs of disease like diabetes and rest.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research